Benchmarking a car-originated-signal approach for real-time electric vehicle charging control

We propose and benchmark a new approach for electric vehicle charging control to match arbitrary power profiles. The approach enables vehicles to compute signals reflecting their need for charge and willingness to supply power. An aggregator collects these signals and implements the control with minor computational effort. We benchmark our approach against a centralized optimization, focusing on the trade-off between objective fulfillment and solving time. For the evaluation, we aim at load leveling in a distribution network with high amounts of solar generation. The scenario is based on electricity demand, solar generation, and car mobility data from Munich, Germany. Our results show that the proposed approach achieves relatively good performance, even for large EV fleets, at a low computational cost. Our approach can be generalized to different loads and objectives and could enable new business models for aggregators.